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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X = digits.data\n",
    "y = digits.target.copy()\n",
    "\n",
    "y[digits.target==9] = 1\n",
    "y[digits.target!=9] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)\n",
    "decision_scores = log_reg.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "precisions = []\n",
    "recalls = []\n",
    "\n",
    "thresholds = np.arange(np.min(decision_scores), np.max(decision_scores), 0.1)\n",
    "for threshold in thresholds:\n",
    "    y_predict = np.array(decision_scores >= threshold, dtype='int')\n",
    "    precisions.append(precision_score(y_test, y_predict))\n",
    "    recalls.append(recall_score(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(thresholds, precisions)\n",
    "plt.plot(thresholds, recalls)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## Precison-Recall 曲线"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(precisions ,recalls)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## sklearn中 的precision-recall曲线"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_test, decision_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(144,)"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "precisions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(144,)"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "recalls.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(143,)"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "thresholds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(thresholds, precisions[:-1])\n",
    "plt.plot(thresholds, recalls[:-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(precisions, recalls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}